构建大规模无线信号无人机检测数据集，支持真实与合成数据混合训练。
CageDroneRF: A Large-Scale RF Benchmark and Toolkit for Drone Perception
- 结合实测与可控合成数据生成，精确控制信噪比与干扰源。
- 覆盖多种主流无人机型号和复杂环境，支持检测与识别任务。
- 提供开源工具链，可适配现有数据集，推动模型可复现性研究。
我们提出CageDroneRF（CDRF），一个基于真实采集与系统化合成变体的大规模射频（RF）无人机检测与识别基准数据集。为解决现有数据集稀缺且多样性不足的问题，CDRF融合大量原始记录与严谨的增强流程，包括：(i) 精确控制信噪比（SNR），(ii) 注入干扰发射源，(iii) 施加频率偏移并保持标签一致性重新计算边界框以支持目标检测。数据集涵盖多种当代无人机型号，其中许多未见于现有公开数据集，且采自罗格斯大学校园及受控射频笼设施。伴随发布的还有可互操作的开源工具链，支持数据生成、预处理、增强与评估，并兼容现有公共基准。该平台支持分类、开放集识别与目标检测的标准化评测，促进可复现的模型比较与研发。通过发布这一综合性基准与工具集，我们旨在加速鲁棒、泛化性强的射频感知模型的发展。
原文摘要 · Abstract (English)
We present CageDroneRF (CDRF), a large-scale benchmark for Radio-Frequency (RF) drone detection and identification built from real-world captures and systematically generated synthetic variants. CDRF addresses the scarcity and limited diversity of existing RF datasets by coupling extensive raw recordings with a principled augmentation pipeline that (i)~precisely controls Signal-to-Noise Ratio (SNR), (ii)~injects interfering emitters, and (iii)~applies frequency shifts with label-consistent bounding-box recomputation for detection. The dataset spans a wide range of contemporary drone models, many of which are unavailable in current public datasets, and diverse acquisition conditions, derived from data collected at the Rowan University campus and within a controlled RF-cage facility. CDRF is released with interoperable open-source tools for data generation, preprocessing, augmentation, and evaluation that also operate on existing public benchmarks. It enables standardized benchmarking for classification, open-set recognition, and object detection, supporting rigorous comparisons and reproducible pipelines. By releasing this comprehensive benchmark and tooling, we aim to accelerate progress toward robust, generalizable RF perception models.
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